Coarse-to-Fine Concept Bottleneck Models
- URL: http://arxiv.org/abs/2310.02116v2
- Date: Thu, 27 Jun 2024 15:53:56 GMT
- Title: Coarse-to-Fine Concept Bottleneck Models
- Authors: Konstantinos P. Panousis, Dino Ienco, Diego Marcos,
- Abstract summary: This work targets ante hoc interpretability, and specifically Concept Bottleneck Models (CBMs)
Our goal is to design a framework that admits a highly interpretable decision making process with respect to human understandable concepts, on two levels of granularity.
Within this framework, concept information does not solely rely on the similarity between the whole image and general unstructured concepts; instead, we introduce the notion of concept hierarchy to uncover and exploit more granular concept information residing in patch-specific regions of the image scene.
- Score: 9.910980079138206
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep learning algorithms have recently gained significant attention due to their impressive performance. However, their high complexity and un-interpretable mode of operation hinders their confident deployment in real-world safety-critical tasks. This work targets ante hoc interpretability, and specifically Concept Bottleneck Models (CBMs). Our goal is to design a framework that admits a highly interpretable decision making process with respect to human understandable concepts, on two levels of granularity. To this end, we propose a novel two-level concept discovery formulation leveraging: (i) recent advances in vision-language models, and (ii) an innovative formulation for coarse-to-fine concept selection via data-driven and sparsity-inducing Bayesian arguments. Within this framework, concept information does not solely rely on the similarity between the whole image and general unstructured concepts; instead, we introduce the notion of concept hierarchy to uncover and exploit more granular concept information residing in patch-specific regions of the image scene. As we experimentally show, the proposed construction not only outperforms recent CBM approaches, but also yields a principled framework towards interpetability.
Related papers
- Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery [52.498055901649025]
Concept Bottleneck Models (CBMs) have been proposed to address the 'black-box' problem of deep neural networks.
We propose a novel CBM approach -- called Discover-then-Name-CBM (DN-CBM) -- that inverts the typical paradigm.
Our concept extraction strategy is efficient, since it is agnostic to the downstream task, and uses concepts already known to the model.
arXiv Detail & Related papers (2024-07-19T17:50:11Z) - LLM-based Hierarchical Concept Decomposition for Interpretable Fine-Grained Image Classification [5.8754760054410955]
We introduce textttHi-CoDecomposition, a novel framework designed to enhance model interpretability through structured concept analysis.
Our approach not only aligns with the performance of state-of-the-art models but also advances transparency by providing clear insights into the decision-making process.
arXiv Detail & Related papers (2024-05-29T00:36:56Z) - Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models [57.86303579812877]
Concept Bottleneck Models (CBMs) ground image classification on human-understandable concepts to allow for interpretable model decisions.
Existing approaches often require numerous human interventions per image to achieve strong performances.
We introduce a trainable concept realignment intervention module, which leverages concept relations to realign concept assignments post-intervention.
arXiv Detail & Related papers (2024-05-02T17:59:01Z) - A Self-explaining Neural Architecture for Generalizable Concept Learning [29.932706137805713]
We show that present SOTA concept learning approaches suffer from two major problems - lack of concept fidelity and limited concept interoperability.
We propose a novel self-explaining architecture for concept learning across domains.
We demonstrate the efficacy of our proposed approach over current SOTA concept learning approaches on four widely used real-world datasets.
arXiv Detail & Related papers (2024-05-01T06:50:18Z) - Separable Multi-Concept Erasure from Diffusion Models [52.51972530398691]
We propose a Separable Multi-concept Eraser (SepME) to eliminate unsafe concepts from large-scale diffusion models.
The latter separates optimizable model weights, making each weight increment correspond to a specific concept erasure.
Extensive experiments indicate the efficacy of our approach in eliminating concepts, preserving model performance, and offering flexibility in the erasure or recovery of various concepts.
arXiv Detail & Related papers (2024-02-03T11:10:57Z) - Sparse Linear Concept Discovery Models [11.138948381367133]
Concept Bottleneck Models (CBMs) constitute a popular approach where hidden layers are tied to human understandable concepts.
We propose a simple yet highly intuitive interpretable framework based on Contrastive Language Image models and a single sparse linear layer.
We experimentally show, our framework not only outperforms recent CBM approaches accuracy-wise, but it also yields high per example concept sparsity.
arXiv Detail & Related papers (2023-08-21T15:16:19Z) - Multi-dimensional concept discovery (MCD): A unifying framework with
completeness guarantees [1.9465727478912072]
We propose Multi-dimensional Concept Discovery (MCD) as an extension of previous approaches that fulfills a completeness relation on the level of concepts.
We empirically demonstrate the superiority of MCD against more constrained concept definitions.
arXiv Detail & Related papers (2023-01-27T18:53:19Z) - Concept Gradient: Concept-based Interpretation Without Linear Assumption [77.96338722483226]
Concept Activation Vector (CAV) relies on learning a linear relation between some latent representation of a given model and concepts.
We proposed Concept Gradient (CG), extending concept-based interpretation beyond linear concept functions.
We demonstrated CG outperforms CAV in both toy examples and real world datasets.
arXiv Detail & Related papers (2022-08-31T17:06:46Z) - Automatic Concept Extraction for Concept Bottleneck-based Video
Classification [58.11884357803544]
We present an automatic Concept Discovery and Extraction module that rigorously composes a necessary and sufficient set of concept abstractions for concept-based video classification.
Our method elicits inherent complex concept abstractions in natural language to generalize concept-bottleneck methods to complex tasks.
arXiv Detail & Related papers (2022-06-21T06:22:35Z) - GlanceNets: Interpretabile, Leak-proof Concept-based Models [23.7625973884849]
Concept-based models (CBMs) combine high-performance and interpretability by acquiring and reasoning with a vocabulary of high-level concepts.
We provide a clear definition of interpretability in terms of alignment between the model's representation and an underlying data generation process.
We introduce GlanceNets, a new CBM that exploits techniques from disentangled representation learning and open-set recognition to achieve alignment.
arXiv Detail & Related papers (2022-05-31T08:53:53Z) - Interpretable Visual Reasoning via Induced Symbolic Space [75.95241948390472]
We study the problem of concept induction in visual reasoning, i.e., identifying concepts and their hierarchical relationships from question-answer pairs associated with images.
We first design a new framework named object-centric compositional attention model (OCCAM) to perform the visual reasoning task with object-level visual features.
We then come up with a method to induce concepts of objects and relations using clues from the attention patterns between objects' visual features and question words.
arXiv Detail & Related papers (2020-11-23T18:21:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.